Hi all!
I'm quite new with the analysis of ChIP-seq and RNA-seq data, and I just started a new project on the analysis of different kind of ENCODE data.
Breifly, my problem is that I have to process multiple alignment files from different ChIP-seq assay (I meant different histone mod.) and convert them into normalized genome-wide signal coverage tracks, similar to the work performed in
ENCODE-wiggler.
Now I have to perform the analysis by my own with some modifications. I have for each ChIP-seq experiment wig files with coverage information (read counts) for each single position. I just wanted to use these files as input to generate density profile using kernel smoothing. Do you think is possibile to use the KernSmooth R package? I thought that this is exactly what I need but the function obviously compute density profile of data values in x, whilst I want to use it on values (tag counts) that are fixed along the chromosomic coordinate.
Any suggestion? Is there maybe another package/function more suitable for this case? thanks a lot in advance.
fran
I'm quite new with the analysis of ChIP-seq and RNA-seq data, and I just started a new project on the analysis of different kind of ENCODE data.
Breifly, my problem is that I have to process multiple alignment files from different ChIP-seq assay (I meant different histone mod.) and convert them into normalized genome-wide signal coverage tracks, similar to the work performed in
ENCODE-wiggler.
Now I have to perform the analysis by my own with some modifications. I have for each ChIP-seq experiment wig files with coverage information (read counts) for each single position. I just wanted to use these files as input to generate density profile using kernel smoothing. Do you think is possibile to use the KernSmooth R package? I thought that this is exactly what I need but the function obviously compute density profile of data values in x, whilst I want to use it on values (tag counts) that are fixed along the chromosomic coordinate.
Any suggestion? Is there maybe another package/function more suitable for this case? thanks a lot in advance.
fran
